Trans-Atlantic Modelling
and Simulation for
Cyber-Physical Systems

TAMS4CPS Glossary

The glossary documents terminology in the field of modelling and simulation (M&S) for cyber-physical systems (CPS). The role of the glossary is to facilitate communication in the range of workshops and outputs that are provided by the TAMS4CPS project.

The glossary gathers terms from various sources, including:

  • The glossary on systems of systems developed by the COMPASS[1] EU project (COMPASS D11.3)
  • Definitions found in deliverables produced by the CyPhERS[2] EU project (CyPhERS D2.1) (CyPhERS D2.2) (CyPhERS D4.1) (CyPhERS D4.2) (CyPhERS D5.1) (CyPhERS D5.2)
  • Definitions found on the CPSoS EU project website and deliverables (CPSoS) (CPSoS D2.4)
  • The taxonomy that underlies the searching facilities of the US CPS virtual organization website (CPS-VO)
  • The cyber-physical systems concept map developed by Asare et al. (Asare et al.).

A targeted literature search was also carried for key concepts of the project (e.g., “architectures principles”, “virtual engineering”, etc.).

The terms have been divided into the following categories:

  • Core terms that are essential for a working knowledge of the area of M&S of CPS
  • Related terms that are relevant to M&S of CPS, but are either more specialised or are not key to understanding the area as a whole
  • Theme-specific terms that are specialised to one or more of the five themes of TAMS4CPS (see below). Note that some terms may be defined differently within different themes. This reflects the varieties of usage in separate communities.

The five themes of the TAMS4CPS project are:

  1. Architectures principles and models for autonomous safe secure cyber-physical systems
  2. Systems design, modelling and virtual engineering for cyber-physical systems
  3. Real time modelling for autonomous adaptive and cooperative cyber-physical systems
  4. MBSE applied to computing platforms and energy management
  5. Integration of socio/legal/governance models within modelling frameworks

Italics are used to indicate other terms that are defined within this glossary.

  • Core terms

The terms provided in this section are considered essential for a working knowledge of the area of M&S of CPS. Figure A1 shows a European view (CyPhERS D5.2) of how CPS relates to other research areas such as embedded systems and systems of systems.

Figure A1: An EU perspective (CyPhERS D5.2) on the relationship between CPS and other related areas.

Abstraction

Models may be abstract “in the sense that aspects of the product not relevant to the analysis in hand are not included” (Fitzgerald and Larsen, 1998). CPS models may reasonably contain multiple levels of abstraction, for representing views of individual constituent systems and for the view of the CPS level. Adapted from (COMPASS D11.3).

Abstraction hierarchies

“a human invention intended to assist people in mastering the complexity of systems by ignoring unnecessary details. They determine successive levels of granularity of observation at which system properties can be studied” (CyPhERS D4.1).

Big data

Big data can be defined as “analytics using data” (CyPhERS D5.2).

The area of big data is very relevant to CPS as “CPS and IoT enable an enormous amount of data related to physical systems to be made available for analysis. Big data is relevant for non technical systems and IT systems, but becomes even more interesting when applied in the context of CPS due to the implications of physicality in terms of capabilities, technical risks and costs” (CyPhERS D5.2).

Boundary

“draws the line between what is inside and what is outside the system. Effectively, the system boundary defines the scope and context of the system” (Holt & Perry, 2008).

Collaboration to compete

A concept where “competitors work together to create new markets, or to expand existing markets in a way that none of the competitors could do on their own.” (CyPhERS D5.1).

Competence (model)

“An ability to achieve a given task or job.

[Relating to modelling activities] We consider a model as competent for a given analysis if it contains sufficient detail to permit that analysis.” (COMPASS D11.3)

Component

See constituent system.

Composability

“The defining characteristic of composability is that different simulation systems can be composed at configuration time in a variety of ways, each suited to some distinct purpose” (Petty & Weisel 2003).

“Two types of composability can be defined: syntactic and semantic… also been called engineering and modeling composability… The question in syntactic composability is whether the components can be connected. In contrast, semantic (modeling) composability is a question of whether the models that make up the composed simulation system can be meaningfully composed, i.e., if their combined computation is semantically valid. It is possible that two components may be syntactically linked, so that one can pass data to the other, but semantically invalid, if the data produced by the first component is not within the bounds of what the other can validly accept” (Petty & Weisel 2003).

“Composability is more than just the ability to put simulations together from components; it is the ability to combine and recombine, to configure and reconfigure, sets of components from those available into different simulation systems to meet different needs” (Petty & Weisel, 2003).

See also dynamic reconfiguration and dynamicity of behaviour.

Composition

The selection and assembly of “simulation components in various combinations into valid simulation systems to satisfy user requirements” (Petty & Weisel 2003a). Taken from (COMPASS D11.3).

“The ability to include models as submodels inside other models” (CPS-VO).

Constituent system (CS, constituent)

A system that is a constituent part of a CPS.

Non preferred alternatives: subordinate system, component system. We prefer “constituent system” to avoid confusion with other uses of the word component. In the US the term component is more commonly used. Adapted from (COMPASS D11.3).

Cyber-Physical System

The term “Cyber-Physical System” appears to have been coined by Helen Gill at the National Science Foundation in the United States in 2006 (Lee, 2015). Several similar but subtly different definitions have been offered. For example:

US:

“Cyber-Physical Systems […] are integrations of computation and physical processes” (Lee, 2007).

“Cyber-Physical Systems […] can be described as smart systems that encompass computational (i.e., hardware and software) and physical components, seamlessly integrated and closely interacting to sense the changing state of the real world. These systems involve a high degree of complexity at numerous spatial and temporal scales and highly networked communications integrating computational and physical components” (Energetics Inc., 2013).

 

Europe:

Cyber-Physical Systems “refer to ICT systems (sensing, actuating, computing, communication, etc.) embedded in physical objects, interconnected (including through the Internet) and providing citizens and businesses with a wide range of innovative applications and services” (EC, 2013).

“Cyber-Physical System are systems with embedded software (as part of devices, buildings, means of transport, transport routes, production systems, medical processes, logistic processes, coordination processes and management processes), which:

  • directly record physical data using sensors and affect physical processes using actuators;
  • evaluate and save recorded data, and actively or reactively interact both with the physical and digital world;
  • are connected with one another and in global networks via digital communication facilities (wireless and/or wired, local and/or global);
  • uses globally available data and services;
  • have a series of dedicated, multi-modal human-machine interfaces”

(acatech, 2011).

“A Cyber-Physical System (CPS) consists of computation, communication and control components tightly combined with physical processes of different nature, e.g., mechanical, electrical, and chemical. Typically a CPS is defined and understood (evaluated) in a social and organizational context” (CyPhERS D2.2).

“Large complex physical systems that are interacting with a considerable number of distributed computing elements for monitoring, control and management which can exchange information between them and with human users” (CPSoS).

An initial overview might suggest that European usages place more emphasis on the “cyber” aspect of CPS, whereas the US definition pays equal attention to both the “cyber” and “physical” part.

Cyber‐physical system of systems (CPSoS)

“Cyber‐physical systems which exhibit the features of systems of systems:

  • Large, often spatially distributed physical systems with complex dynamics
  • Distributed control, supervision and management
  • Partial autonomy of the subsystems
  • Dynamic reconfiguration of the overall system on different time‐scales
  • Possibility of emerging behaviours
  • Continuous evolution of the overall system during its operation” (CPSoS).

Embedded software

“Software designed for computational processes that interact with the physical processes” (CPS-VO).

Embedded systems

“Embedded Systems are electronic products, equipment or more complex systems containing computing devices that are not externally visible and are generally inaccessible by the user. They are in the electronic key for your car and in the control systems for a nuclear power plant. Embedded Systems enable an every-day object to become a smart object able to communicate with other smart objects either directly or via a network, such as the Internet. Embedded Systems form the edges of the ‘Internet of Things’ – they bridge the gap between cyber space and the physical world of real ‘things’.” (ARTEMIS SRA, 2011).

Embedded systems can be considered to be the “software and electronics part of a CPS and/or IoT system/product”. CPS can be considered to be an expansion of embedded systems that “adds a stronger focus on and inclusion of physical parts”. Similarly “IoT adds internet” (to embedded systems). (CyPhERS D5.2).

Exhaustive

“A test suite is exhaustive if the system under test conforms to the specification whenever all tests in the test suite have been passed” (COMPASS D11.3).

Heterogeneity

Constituent systems are often drawn from different domains, and are modelled in a variety of languages, with different notations, concepts, levels of abstraction, and semantics, which are not necessarily easily mapped one to another. This heterogeneity presents a significant challenge for modelling and simulation in CPS. Adapted from (COMPASS D11.3).

Human-Centric Cyber-Physical Systems (HC2PS)

­CPS developed where “the innovation is human driven, also termed development with humans in the loop, and the thus resulting systems are called Human-Centric Cyber-Physical Systems (HC2PS)” (CyPhERS D2.1).

Internet of Things

Internet of Things (IoT) “emphasizes sensing of the physical world and internet connectivity […]. IoT moreover emphasizes uniquely identifiable things to provide data over internet with limited or no human interaction” (CyPhERS D5.2).

“IoT can be seen as a bottom-up enabling technology, which can be used to create a special class of CPS, i.e. systems including the internet.   Conceptually, “an internet of things” will be part of one or more CPS; however when referring to the underlying technology, we see this as different compared to a system – motivating to have a part of IoT which is disjoint from CPS. Some visions of the IoT go beyond basic communication and consider the ability to link “cloud” representations of the real things with additional information (such as location, status, and business related data) and services.   […] we consider all IoT systems to be CPS, while there are Cyber-Physical Systems that need not use the internet” (CyPhERS D5.2).

Mechatronics

“the   synergistic combination   of   mechanical   and electrical engineering, computer science,   and   information   technology, which includes control systems as well as numerical methods used to design products with built-in intelligence” (Wikander , 2001)[3].

Model

“A partial description of a system, where the description is limited to those components and properties of the system that are pertinent to the current goal” (COMPASS D11.3).

Model-based design

Model-based design exploits mathematical and executable models, and is at the heart of many methodologies for system level integration (CyPhERS D5.1).

Modelling

“The activity of creating models” (Fitzgerald et al., 2014).

Simulation

“The imitation of the operation of a real-world process or system over time” (CPS-VO).

With relation to modelling, simulation is the “symbolic execution of a model” (Fitzgerald et al., 2014) or “a model that behaves like a given system when provided a set of controlled inputs” (ISO/IEC/IEEE 24765:2010).

System

“A combination of interacting elements organized to achieve one more stated purposes” (INCOSE 2011).

“An interacting combination of system elements that work together to achieve a set of goals and satisfy a set of needs” (COMPASS D11.3).

System context

Systems […] are commonly decomposed into a hierarchical series of models, that represent the whole at different level[s] of abstraction and detail. The system context is a set of points of view based on the level of hierarchy of a system […]. Each hierarchical level will have one or more contexts associated with it that consider the relevant requirements from the appropriate point of view, trace back to requirements at the higher level and establish the meaning of the requirements in that context” (COMPASS D11.3).

System element

“Every system is composed of system elements” (COMPASS D11.3).

In the context of CPS, the system elements are considered to be constituent systems. Adapted from (COMPASS D11.3).

System environment

The environment of a system is “all that exists outside the boundary of the system” (Henrie & Delaney, 2005). The environment interacts with the CPS, providing it with inputs and/or events. For CPS, where the environment boundary lies can be subjective, e.g., is the system operator part of the CPS or part of the environment? Adapted from (COMPASS D11.3).

See also boundary.

System of interest

“The system being developed by the project at hand” (Holt & Perry, 2008).

Defining the boundaries and environment of a system is part of defining the system of interest. Adapted from (COMPASS D11.3).

See also system under test (SUT).

System of systems

“A System of Systems (SoS) is a collection of constituent systems that pool their resources and capabilities together to create a new, more complex system which offers more functionality and performance than simply the sum of the constituent systems” (COMPASS D11.3)

CyPhERS (CyPhERS D5.2) consider SoS to be a “special class of CPS” that “focus on evolutionary large scale systems and co-ordination among involved systems, which may or may not include CPS (in practice, most SoS will be CPS!)”.

System under test

“The system currently being tested for correct behaviour. An alias for system of interest, from the point of view of the tester” (COMPASS D11.3).

See also system of interest.

Test case

In the context of model-based testing Utting et al. define a test case as “a finite structure of input and expected output” (Utting et al, 2006).

In TAMS4CPS we take a broader view of a test case: it should comprise a sufficiently detailed description to allow others to construct models and either a set of experimental data, or a sample of results from other models against which the modeller can test their method and/or computational model (for instance). The test case must also provide a measure of confidence in the provided results, so that the modeller can reliably determine the accuracy or reliability of the approach under development. The role of a test case is for M&S validation, evaluation, and benchmarking.

Test model

“Specifies the expected behaviour of a system under test. This is an important step in model based testing (MBT)” (COMPASS D11.3).

In the context of TAMS4CPS a test model may form part of a test case.

See also model based testing.

Testing

“A technical operation or procedure that consists of determination of one or more characteristics of a given product, process or service according to a specified procedure” (CPS-VO).

Validation

In the context of modelling, validation can be defined as:

“those activities which increase the modeller’s and the customer’s confidence in a model. There are two aspects to this:

  • checking that the model is internally consistent, i.e. that the definitions are meaningful (for example, that expressions are not undefined and that functions do not allow invariants to be broken);
  • checking that the model accurately represents the required behaviour of the system being modelled” (Fitzgerald & Larsen, 1998).

Verification

“The verification process confirms that the system of interest and all its elements perform their intended functions and meet the performance requirements allocated to them (i.e., that the system has been built right)” (INCOSE, 2011).

  • Related terms

The terms provided in this section are considered to be relevant to M&S of CPS, but more specialised and not key to understanding the area as a whole.

Acknowledged system of systems (SoS)

“Acknowledged SoS have recognized objectives, a designated manager, and resources for the SoS, however, the constituent systems retain their independent ownership, objectives, funding, as well as development and sustainment approaches. Changes in the systems are based on collaboration between the SoS and the system” (Dahmann & Baldwin, 2008).

Ambient Intelligence

Ambient intelligence “is a vision of the Information Society where the emphasis is on greater user-friendliness, more efficient services support, user-empowerment, and support for human interactions. People are surrounded by intelligent intuitive interfaces that are embedded in all kinds of objects and an environment that is capable of recognising and responding to the presence of different individuals in a seamless, unobtrusive and often invisible way” (ISTAG, 2001).

Artificial intelligence

Artificial intelligence “is a branch of information technology concerned with the automation of smart behaviour” (CyPhERS D5.1).

Atomicity

An atomic action “has the property of indivisibly advancing the state of a computation” (Campbell & Randell, 1986).

Autonomy

Each constituent system “can function as a free and self-governing system that can make individualistic and self-supporting decisions to optimise its own outcome” (Nielsen et al 2013). Note this is not necessarily the same as an autonomous system. Adapted from (COMPASS D11.3).

CyPhERS provide a similar definition, but relates to control: “the system’s property of being sufficiently independent in controlling its own structural and behavioural properties” (CyPhERS D5.1).

See also autonomous systems.

Autonomous systems

“computing systems that can manage themselves given high-level objectives from administrators… The essence of autonomic computing systems is self-management” (Kephart & Chess, 2001).

Bio-cyber- systems

“are a combination of biological parts and computing parts” (CyPhERS D2.2)

Black box

“A blackbox constituent system has to be integrated into a CPS without allowing any changes to be made to the constituent system”. Adapted from (COMPASS D11.3).

See also white box and grey box.

Capability

“Describes the ability to do something in order to deliver stated goals.” (COMPASS D11.3).

Cognitive cyber‐physical systems of systems

“Systems of Systems (SoS) by their very nature are large, distributed and extremely complex presenting a myriad of operational challenges. To cope with these challenges there is a need for improved situational awareness. Gaining an overview of the entire SoS is inherently complicated by the presence of decentralized management and control. The introduction of cognitive features to aid both operators and users of complex cyber-physical systems of systems is seen as a key requirement for the future to reduce the complexity management burden from increased interconnectivity and the data deluge presented by increasing levels of data acquisition” (CPSoS D2.4).

Collaborative networks

A Collaborative Network is a network consisting of a variety of entities that are autonomous, geographically distributed, and heterogeneous, that collaborate to better achieve common or compatible goals, supported by computer network (Camarinha-Matos & Afsarmanesh, 2008).

The collaborative networks taxonomy includes:

  • Networking “communication and information exchange among participants for mutual benefit”
  • Coordinated Networking “in addition… involves aligning/altering activities so that more efficient results are achieved. Coordination, that is, the act of acting together harmoniously”
  • Cooperation “…also sharing resources for achieving compatible goals… Although participants mostly work apart, each one focusing a specific task, these tasks represent a decomposition of a larger process (e.g., to produce a complex product) and from time to time required synchronization and interaction”
  • Collaboration “entities share information, resources and responsibilities to jointly plan, implement, and evaluate a program of activities to achieve a common goal… can also give to an outside observer the image of a joint identity”

Taken from (COMPASS D11.3).

Collaborative system of systems (SoS)

The SoS has no coercive power over the constituent systems, but they voluntarily choose to collaborate in order to achieve the SoS goals (Maier 1998). Taken from (COMPASS D11.3).

Component-based software engineering

A system constructed using components which are reusable and which “are required to interact with each other in a system architecture” [Jifeng et al 2005]. A “component” is defined by Hasselbring as “a unit of composition with contractually specified interfaces and explicit context dependencies only. A software component can be deployed independently and is subject to third-party composition” [Hasselbring 2002].

In the context of CPS a “component” may also refer to a constituent system, which exhibits varying degrees of autonomy, independence, evolution, distribution, dynamicity, emergence, interdependence and interoperability. Adapted from (COMPASS D11.3).

Complexity

Complexity is frequently viewed as a problem related to multiple relationships between entities: “the more relationships that are added between system elements, the higher the complexity of the overall system… The complexity of the whole is certainly greater than the complexity of the sum of its parts” (Holt & Perry, 2008).

Complexity may also be attributed to a “‘lack of knowledge’ or lack of understanding about or within a complex system, domain, environment or solution” (Henrie & Delaney, 2005). Adapted from (COMPASS D11.3).

Computation independent model

A computation independent model is produced at the first stage of the Model-Driven Architecture approach. It “captures detailed requirements but no functionality” (CyPhERS D5.1).

See also platform independent model, platform specific model.

Concurrency

“Concurrent systems.. consist of many components which may execute in parallel, and… complexity arises from the combinations of ways in which their parts can interact” (Schneider, 1999).

Conformance

“Specifies the similarities between the system under test (SUT) and the specification model. The SUT conforms to the specification model A if and only if all input traces result in the same output traces as for A and the interleaving of inputs and outputs is the same for SUT and A.” (COMPASS D11.3).

Context

“A context may be thought of as a ‘point of view’ on the system under development” (COMPASS D11.3). It is possible to view the needs of a CPS from any number of different points of view, so it is essential that it is well understood from which each context is taken. Adapted from (COMPASS D11.3).

Contract

“Contracts are descriptions of the constituent systems of a SoS given in terms of their expectations and the obligations placed on their behaviour. A contract on an operation, therefore, asserts that, given a state and inputs which satisfy the precondition, the operation will terminate and will return a result that satisfies the postcondition and respects any required invariant properties” (Payne & Fitzgerald, 2010). Contracts can be equivalently defined for CPS.

“In the design-by-contract paradigm, the emphasis is on specifying the interfaces between components, usually involving preconditions, postconditions, and state invariants to document assumptions and commitments. More sophisticated forms of contract deal with concurrency and shared resources.” (COMPASS D11.3).

In the context of CPS, contracts formalize the notion of interfaces between models and tools in the design flow. Contracts can offer a natural framework to reason about distributed control architectures as well as the heterogeneous interface between the cyber component and its physical counterpart” (CyPhERS D4.1).

Cross-cutting

Areas of concern which intersect with other concerns are referred to as cross-cutting concerns (Rashid et al., 2003, Elrad et al., 2001, Elrad et al., 2003). Whether a concern cross-cuts another is largely defined by the system’s general decomposition (Elrad et al., 2003). For example, where two concerns relevant to a system (concerns A and B) intersect, the system designed using conventional techniques could be built around concern A, in which case concern B becomes the cross-cutting concern. Or the system could be built around concern B, in which case concern A introduces the cross-cutting. Taken from (COMPASS D11.3).

Dependability

“The ability to deliver service that can justifiably be trusted” (Avizienis et al., 2004).

Dependency

“A dependency is used to show that one block is dependent on another. This means that a change in one block may result in a change in its dependent block” (Holt & Perry, 2008). This definition comes from SysML modelling, where a block represents an entity in a system. For a CPS, we may see dependencies between constituent systems. Adapted from (COMPASS D11.3).

Design principle

“A normative principle on the design of an artifact. As such, it is a declarative statement that normatively restricts design freedom” (Greefhorst and Proper, 2011).

Directed system of systems (SoS)

An SoS built and managed to fulfil specific goals. Although the constituents can operate independently, within the SoS they accept some central management to ensure that SoS-level goals are met (Maier 1998). Taken from (COMPASS D11.3).

Distributed controllers

“control systems or networks whose signal-processing components are geographically dispersed and may even be hierarchically structured, rather than being organized centrally” (CyPhERS D5.1).

Distribution

“The constituents … are dispersed and scattered from each other such that a type of connectivity is needed to establish relations that will enable communication and information sharing” (Nielsen et al 2013).

Dynamic Reconfiguration

Dynamic reconfiguration refers to “runtime changes to a system’s architectural topology (or configuration) – that is the collection of components composing a system and the connections between them” (Payne, 2012)

Dynamic reconfiguration is a possible tactic for coping with an adverse change in the operational environment. For example, if a CPS’s constituent system becomes unavailable suddenly (withdraws from the CPS) another CS can be located and used to provide an alternative service. Adapted from (COMPASS D11.3).

See also dynamicity of behaviour.

Dynamicity of behaviour

An SoS’s (or CPS’s) “ability to change the relations between constituents and adjust the number of constituents contained within. Either the individual constituents or the SoS as a whole, have facilities that enable flexible topology of the SoS” (Nielsen et al 2013).

Element Type

“Element types represent types of view element occurring in a model. Element types may represent underlying modelling element types from the modelling language being used, such as a block if using SysML or a class if using UML. They may also represent defined conceptual elements used on views.” (COMPASS D11.3). This definition has been provided in the context of SysML models (where a block represents an entity of a system), however, types are relevant in other models as well.

Emergence of behaviour

“Increased capabilities arise from synergistic collaboration between the individual systems in order to deliver a higher functionality than delivered by the systems separately” (Nielsen et al 2013).

“Due to local autonomy and dynamic interactions, cyber-physical systems of systems can realize self-organization and exhibit structure formation and system-wide instability, in short, emergent behaviour” (CPSoS D2.4).

Event

“Events are considered to be atomic and indivisible in their occurrence. However, a single event may still contain various pieces of information, so events can have some structure” (Schneider, 1999).

Evolution

The “system’s ability to benefit from a varying number of constituents and relations, as well as its ability to gain from the adjustments of the individual constituents’ capabilities over time” (Nielsen et al 2013).

Failure

“Correct service is delivered when the service implements the system function. A service failure, often abbreviated here to failure, is an event that occurs when the delivered service deviates from correct service. A service fails either because it does not comply with the functional specification, or because this specification did not adequately describe the system function” (Avizienis et al., 2004).

In a conventional systems setting, a failure of a component can lead to a fault in the system. For CPS, we interpret a failure of a constituent system as a fault in the CPS. Adapted from (COMPASS D11.3).

Fault tolerance

“to avoid service failures in the presence of faults” (Avizienis et al., 2004).

Functional requirements/properties

“Functional properties (FPs) are those that pertain to the functional correctness of the system. For example, the relation between system variables before and after a computation may be described as a functional property” (Payne & Fitzgerald, 2010).

See also requirements.

Goal

“A goal is an objective the system under consideration should achieve. Goal formulations thus refer to intended properties to be ensured; they are optative statements as opposed to indicative ones” (van Lamsweerde 2001).

Grey box

“A greybox constituent system is characterized by allowing dynamic installation of SoS applications, which function as integration code between the greybox constituent system and the SoS. An example of such an environment is an Android based constituent system allowing loading of Android applications onto the constituent system” (COMPASS D21.2). This term was defined in the context of SoS, but applies equally well to constituent systems of a CPS.

See also black box and white box.

Independence

Adapted from the context of systems of systems (Nielsen et al 2013):

The ability of constituents to operate self-sufficiently when detached from the rest of the CPS.

Integration

Adapted from the context of systems of systems (Jamshidi, 2008):

Integration of CPS implies that each system can communicate and interact (control) with the CPS regardless of their hardware, software characteristics, or nature. This means that they need to have the ability to communicate with the CPS or a part of the CPS without compatibility issues such as operating systems, communication hardware, and so on.

Intention recognition

“the ability to recognize the intentions of an agent ([…] the term “agent” refers to a human being or a technological system) by analysing their previous behaviour or the effect of this behaviour on the environment” (CyPhERS D5.1).

See also plan recognition.

Interdependence

Adapted from the context of systems of systems (Nielsen et al 2013):

There is a mutual dependency between the constituents that form the CPS. This arises from constituents having to rely on each other in order to fulfil the common goal of the CPS. Therefore the actions of the constituents impact the others. If the objective of a constituent depends on another constituent in the CPS, then it might be a requirement by the CPS that this constituent itself contributes and sacrifices some of its individual behaviour before it can gain from the CPS.

Interface

“Defines the boundary across which two entities meet and communicate with each other” (COMPASS D11.3).

Interface definition language

“Interface definition languages (IDLs), as well as typed object-based or object-oriented languages, let the component designer specify

  • the operation a component can perform
  • the input and output parameters each component requires, and
  • the possible exceptions that might be raised during operation”

(Beugnard et al., 1999).

Interoperability

Adapted from the context of systems of systems (Nielsen et al 2013):

The ability of the CPS to incorporate a wide range of heterogeneous constituents into a collaborative collection. This involves the integration and adaptation of interfaces, protocols and standards to enable bridging between legacy and newly designed systems. The term is not limited to a design phase of the CPS, but applies throughout its lifetime.

Life cycle

The life cycle describes the evolution of a CPS (or system) over time. A system or CPS may have any number of life cycles associated with it, depending on the context: e.g., product life cycle; project life cycle; acquisition life cycle; operational life cycle, etc.

Life cycles interact with one another via life cycle interaction points. Any life cycle is made up of one or more stages.

Adapted from (COMPASS D11.3).

Life cycle interaction point

“A life cycle interaction point defines a specific point at which one, more than one life cycle interacts with another” (COMPASS D11.3).

Megainfrastructure

The combined infrastructure “said to be emerging from the convergence of energy, telecommunications, transportation, the Internet, and electronic commerce” (CyPhERS D4.2).

Model-based testing

In a model-based testing approach, “the behaviour of the system under test (SUT) is specified by a model elaborated in the same style as a model serving for development purposes. Optionally, the SUT model can be paired with an environment model restricting the possible interactions of the environment with the SUT.” (Peleska 2013). For CPS, the system under test would refer to a CPS. Adapted from (COMPASS D11.3).

See also system under test and environment.

Need

“A need describes something that can be given meaning by a use case. A good example of this is a requirement, where a use case would be defined as a requirement that has been put into context” (COMPASS D11.3).

Non-deterministic specification

“The constructs which always yield unique result are determinate, those which may yield different results when invoked several times nondeterminate. The presence of a nondeterminate construct in an expression does not force the corresponding operation to be non-deterministic. Determinacy implies determinism but nondeterminacy does not necessarily imply nondeterminism” (Meldal & Walicki 1995).

See also abstraction and underspecification.

Non-functional requirements/properties

“Non-functional properties (NFPs) pertain to characteristics other than functional correctness. For example, reliability, availability and performance of specific functions or services are NFPs that are quantifiable. Other NFPs may be more difficult to measure” (Payne & Fitzgerald, 2010).

See also functional requirements and requirements.

Normative Principle

“A declarative statement that normatively prescribes a property of something” (Greefhorst and Proper, 2011).

Pattern recognition

“An IT discipline with a strong engineering component that involves the use of algorithms and systems to recognize patterns in incoming data, compare them against known patterns and assign the detected patterns to different categories” (CyPhERS D5.1).

Physical awareness

“The ability to detect and recognize objects and the physical environment (physical awareness) is a key capability of Cyber-Physical Systems. In particular, it provides the basis for the subsequent analysis of application situations, including all of the technological and human actors involved and their condition, goals and options” (CyPhERS D5.1).

Plan recognition

Plan recognition “goes one step further than intention recognition by using an agent’s past behaviour to predict its future behaviour” (CyPhERS D5.1).

See also intention recognition.

Platform

“Hardware architecture and a software framework, where the combination allows software to run” (CPS-VO).

Platform-based design

“A paradigm that allows reasoning about design in a structured way. In it, design progresses in precisely defined abstraction levels; at each level, functionality (what the system is supposed to do) is strictly separated from architecture (how the functionality can be implemented). Differently than model-based development, platform-based design consists of a meet-in-the-middle approach where successive top-down refinements of high-level specifications across design layers are mapped onto bottom-up abstractions and characterizations of potential implementations. Each layer is defined by a design platform, which is a library (collection) of components, models, representing functionality and performance of the components and composition rules.” (CyPhERS D4.1).

See also model-based design.

Platform independent model (PIM)

A platform independent model refines a computation independent model, and is “used to specify the functionality of the system without committing to any particular platform” (CyPhERS D5.1).

See also computation independent model, platform specific model.

Platform specific model (PSM)

A platform specific model is derived from a platform independent model (PIM) “through a mapping that consists of model transformations, i.e., rules or algorithms that take objects in the PIM model language and generate (one or more) objects in the PSM model language. Annotations and attributes can be used to enrich the PSM model with non-functional properties” (CyPhERS D5.1).

See also computation independent model, platform independent model.

Privacy by Design

A design philosophy that takes “privacy considerations into account right from the outset […] and involves the inclusion of privacy requirements in all phases of a system’s life cycle, from its conception and design to its implementation, configuration and continued development” (CyPhERS D4.1).

Process

“A series of actions of steps taken in order to achieve a particular end” (COMPASS D11.3).

In architectural modelling, “a process describes the approach that will be adopted to achieve some end point. A process is made up of one or more activity, one or more artefact, and one or more stakeholder” (COMPASS D11.3).

In formal modelling, such as CSP (Communicating Sequence Processes), a process is formal object that represents a behaviour pattern. It is made up of sequences of atomic events composed using a formally defined set of operators, including varieties of choice and communication. In CSP, a process “is completely described by the way it can communicate with its external environment” (Roscoe 2010). Adapted from (COMPASS D11.3).

Refinement

Refinement has a very precise definition in formal modelling, the definition for CML is typical: “A CML process P is refined by a CML process Q if every observation of Q is a possible observation of P. In this respect, if Q is (the model of) a proposed implementation of a given specification P, for example, then refinement guarantees that a user that agreed on the specification P has to be satisfied by Q because every observation of the behaviour of Q is in accordance with the behaviours prescribed by P. Embedded in this view is reduction of non-determinism. An abstract specification P typically embeds some non-determinism to express freedom of design and implementation. Refinement reduces this non-determinism as it moves towards more specific architectural designs and patterns of implementation” (COMPASS D22.5)

Architectural modelling tends to have a broader view on refinement, “the process of transforming one model element (such as views or view elements) into one or more other model elements that are closer to the target solution model, often but not exclusively at a lower level of abstraction. Refinement will typically take place in two ways:

  • Between different levels of abstraction, for example between the requirements-level views and architectural-level views, where transformation occurs.
  • In a single level of abstraction where more detailed aspects of the model are explored, for example, from use cases to scenarios in the requirement-level views, or between scenarios where transformation occurs” (COMPASS D22.5)

Adapted from (COMPASS D11.3).

Reliability

An established general definition of reliability is the “continuity of correct service” (Avizienis et al., 2004).

It is also often defined as a metric, “the probability of a system operating without error for a given time and in a given environment” (CyPhERS D4.1).

See also dependability.

Reluctant system of systems (SoS)

“The SoS has no coercive power over the CS and they don’t voluntarily choose to collaborate in a given SoS to achieve the SoS goals” (introduced as “hostile” SoS type in COMPASS D21.2). Taken from (COMPASS D11.3).

Requirement

“1. a condition or capability needed by a user to solve a problem or achieve an objective.

  1. a condition or capability that must be met or possessed by a system, system component, product, or service to satisfy an agreement, standard, specification, or other formally imposed documents
  2. a documented representation of a condition or capability as in (1) or (2)
  3. a condition or capability that must be met or possessed by a system, product, service, result, or component to satisfy a contract, standard, specification, or other formally imposed document” (ISO/IEC/IEEE 24765:2010).

See also functional requirements, need, non-functional requirements and requirements engineering.

Requirements engineering

A discipline that aims to improve requirement traceability and reduce misinterpretation of requirements “by paying close attention to the management of the requirement descriptions and traceability support and by inserting whenever possible precise formulation and analysis methods and tools” (CyPhERS D5.1).

See also requirement.

Resilience

“Dictionaries commonly define resilience as the ability to `recover quickly from illness, change, or misfortune’, one suggestive synonym being buoyancy or a bouncing quality… it is easier to recover from a potentially destabilising disturbance if it is detected early. [ …] As a result of this, the definition of resilience can be modified to be the ability of a system or an organisation to react to and recover from disturbances at an early stage, with minimal effect on the dynamic stability” (Hollnagel 2006). Adapted from (COMPASS D11.3).

Resilience can also be considered to be a synonym for fault tolerance (Avizienis et al., 2004).

Systems are considered to exhibit resilience if they “maintain state awareness and an accepted level of operational normalcy in response to disturbances, including threats of an unexpected and malicious nature” (CPS-VO).

See also fault tolerance.

Resource

“A resource is anything that is used by an activity within a process. Types of resource include: a person, a room, etc.” (COMPASS D11.3).

Robotics

A branch of science dealing with “automated machines that can take the place of humans in dangerous environments or manufacturing processes, or resemble humans in appearance, behavior, and/or cognition” (CPS-VO).

Safety

The “absence of catastrophic consequences on the user(s) and the environment” (Avizienis et al., 2004).

See also dependability.

Scenario

“An example execution of a use case” (COMPASS D11.3).

In architectural modelling, “a scenario is defined as an exploration of a use case. Each use case will give rise to a number of different situations that may arise. […] A scenario may be realised through, for example, sequence diagrams that show interactions between elements in the system, through using text as a set of scenario steps, or a combination of the two. The formality of the scenario can be increased by using parametric constraints and their usages to permit a mathematically-based approach to understanding the use cases. Scenarios may be created for specific contexts.” (COMPASS D11.3)

In formal modelling, “a use case can be modelled and explored using a variety of techniques. For example, an action can be created that models a series of steps, events, and outputs. [… Scenarios] can be used in simulations and can be verified.”

Security

“A composite of the attributes of confidentiality, integrity and availability, requiring the concurrent existence of 1) availability for authorized actions only, 2) confidentiality, and 3) integrity with “improper meaning “authorized” ” (Avizienis et al., 2004).

Self-defining

A system is self-defining if it “has the ability of deriving knowledge of its components, status, ultimate capacity, and operational situations” (CyPhERS D5.1).

Self-healing

A system is self-healing if it “is able to detect errors or other anomalies and then to resolve appropriate fault tolerance or fault treatment measures” (CyPhERS D5.1).

Self-optimizing

A system is self-optimizing if it “can tune its own configuration and workflow for achieving some goals in the most efficient or effective way” (CyPhERS D5.1).

Self-protecting

A system is self-protecting if it “can detect, identify, and protect itself against malicious attacks and maintain the overall system security and integrity” (CyPhERS D5.1).

Sensor fusion

“The fusion of data from several different sensors in order to obtain more accurate measurements or higher-order data. Sensor fusion is used to detect and correct erroneous measurements made by individual sensors, as well as to make inferences about the system status that are only possible using several sensors” (CyPhERS D5.1).

Sensory swarm

A large number of simple systems that “reproduces swarms in nature; in the animal world survival of some species is based on large numbers that provide safety and reliability of the ecosystem” (CyPhERS D2.2).

Smart City

“A smart city is a place where the traditional networks and services are made more efficient with the use of digital and telecommunication technologies, for the benefit of its inhabitants and businesses. […] The smart city concept goes beyond the use of ICT for better resource use and less emissions. It means smarter urban transport networks, upgraded water supply and waste disposal facilities, and more efficient ways to light and heat buildings. And it also encompasses a more interactive and responsive city administration, safer public spaces and meeting the needs of an ageing population” (EC, 2015).

Smart Grid

A Smart Grid is “a complex web of relationships involving not just the electrical and information infrastructures but also governments,   markets,   customers   and   community   values and beliefs. And the proper design and operation of such systems require attention to the integration of all parts involved. This interwoven   web   of   relationships   covers   a   broad   spectrum   of technical details which goes all the way from market prices “down to the wire” of ohm’s law. However, due to our finite ability to grasp the total reality of electric grids we need constantly to develop better   models, tools and frameworks which will minimize the shortcoming of previous attempts. A good and appropriate design of a future smart grid is one which acknowledges the variety of relationships and provides the service to society in a way which makes humans to flourish. When the technical, economical, environment and civil society aspects are integrated in balanced way the system will achieve the designed goals” (Ribeiro, 2011).

A US definition of Smart Grid is “Modernized electrical grid automated to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity” (CPS-VO).

Stakeholder

In systems engineering, a stakeholder is “anyone who should have some direct or indirect influence on the system requirements” (Sommerville 2001).

COMPASS extended this definition for an SoS context, to emphasise the wide variety of stakeholders involved in SoS engineering:

“Anyone who should have some direct or indirect influence on the SoS requirements. Stakeholders include end-users who will interact with the SoS and everyone else within the boundaries of the SoS who will be affected by it. Engineers who are developing or maintaining other related [systems] or constituent systems, business managers, domain experts, trade union representatives, and so on may also be SoS stakeholders” (COMPASS D11.3).

This extended definition is also applicable to CPS if you replace SoS with CPS throughout.

State

“A collection of variables which represent the state of the system, each variable having a type. The state represents the persistent data: the information that is stored between occurrences of operations and which is read or modified by operations” (Fitzgerald and Larsen 1998).

Substitutability

Constituent systems “may be replaced by alternative systems or assemblies that offer the same or substitutable functionality with weaker or equivalent preconditions and stronger/equivalent postconditions” (Payne & Fitzgerald, 2010). Taken from (COMPASS D11.3).

Synthetic biology

“The construction of biological devices, i.e., molecules and/or biological structures which are designed from a set of basic pre-designed libraries of biological components” (CyPhERS D4.2).

Systems engineering

The FAA present systems engineering as a need to the look at the system as a whole, rather than at its components, and at both social and technical aspects (FAA, 2006). Eisner emphasises a top-down view (Eisner, 2002). INCOSE emphasise multi-disciplinary approaches (INCOSE 2011). Most definitions also note that the end goal of systems engineering is to ensure satisfaction of requirements. We employ the INCOSE definition:

“Systems engineering is an interdisciplinary approach and means to enable the realisation of successful systems” (INCOSE, 2010) (INCOSE, 2011).

Taken from (COMPASS D11.3).

System of systems engineering (SoSE)

COMPASS adapted the definition of systems engineering from INCOSE to define systems of systems engineering as “an interdisciplinary approach and means to enable the realisation of successful systems of systems” (COMPASS D11.3).

However, Meyers et al. observe that there are difficulties in simply applying a traditional system engineering view to the top level of the SoS, because “there are conflicts (funding, management, and system engineering, for example) that prevent such an approach from succeeding” (Meyers et al., 2006). Adapted from (COMPASS D11.3).

See also systems engineering.

Trace structure

A representation of a component or interface with two sets of behaviours. The set of successes are those behaviours which are acceptable and guaranteed by the component. Conversely, the set of failures are behaviours which drive the component into unacceptable states, and are therefore refused” (CyPhERS D5.1).

Traceability

“Requirements traceability refers to the ability to describe and follow the life of a requirement, in both a forwards and backwards direction” (Gotel & Finkelstein, 1994).

Tracing for a CPS may need to incorporate some additional traces, to enable dependencies between cross-organisational boundaries to be identified (e.g., conflicting requirements, change impact). For CPS there may also be multiple domain-specific models involved in the development process and tracing between these models may be required. Adapted and extended from (COMPASS D11.3).

Trust

“The concept of dependence leads to that of trust, which can very conveniently be defined as accepted dependence” (Avizienis et al., 2004).

See also dependability.

Underspecification

“Each model of the specification is a standard (deterministic) structure but we do not identify one unique model. We then speak of underspecification. Later in the development process we may add more properties, whenever we find it appropriate, and so restrict the model class. Thus underspecification functions also, like nondeterminism, as a means of abstraction” (Meldal & Walicki 1995).

“Meldal & Walicki 1995 note that, like nondeterminism, underspecification leaves open the possibility of choosing among several admissible models, but, whilst underspecification admits a choice between different models, nondeterminism admits choices within one model” (COMPASS D11.3).

See also abstraction and non-deterministic specification.

Usability

The “degree to which a product or system can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use” (ISO/IEC 25010:2011).

Use case

“A use case represents a user’s view of a system’s behaviour. It begins with the user initiating some task, and ends with the user achieving their goal. A good example of this is a requirement, where a use case would be defined as a requirement that has been put into context. This can also apply to a number of needs, such as goals and capabilities” (COMPASS D11.3).

Virtual system of systems (SoS)

Virtual SoS lack a central management authority and a centrally agreed-upon SoS-level goal. Large-scale behaviour emerges and may be desirable – but there is no visible active management of the SoS or its goals (Maier 1998). Taken from (COMPASS D11.3).

White box

“A whitebox constituent system requires and allows code changes in the constituent system to enable it to be integrated into a given SoS. Such integrations could, as an example, be performed by the introduction of wrapper components in the constituent system” (COMPASS D21.2). This term was defined in the context of SoS, but applies equally well to constituent systems of a CPS.

See also black box and grey box.

  • Theme-specific terms

The terms provided in this section are considered to be specialised to one or more of the five themes of TAMS4CPS. Note that some terms may be defined differently within different themes. This reflects the different usage of the terms within these separate communities.

  • Architectures principles and models for autonomous safe secure cyber-physical systems

This theme covers all aspects of systems architecting, but particularly focuses on development of modular and composable architectures that take account on non-functional aspects, such as safety and security. Eventually, such models must include the human element in a disciplined fashion and may be used to support assurance and even certification requirements. The area of developing and agreeing standards is particularly important for this theme.

Architectural framework

“A defined set of viewpoints and an ontology. The architectural framework is used to structure an architecture from the point of view of a specific industry, stakeholder role set, or organisation. The architectural framework is defined so that it meets the needs defined by its architectural framework concerns. An architectural framework is created so that it complies with zero or more standards.” (COMPASS D11.3).

Architectural patterns

“We consider the concepts of architectural patterns and architectural styles to be synonymous.” (COMPASS D11.3).

See also architectural styles.

Architectural mismatch

“Many of the hardest problems are best understood as architectural mismatch problems. Each component makes assumptions about the structure of the environment in which it is to operate. Most if not all of these assumptions are implicit, and many of them are in conflict with each other” (Garlan et al 1995).

Architectural style

An architectural style “defines a family of such systems in terms of a pattern of structural organization” (Garlan & Shaw 1994). Describing CPSs in terms of architectural style facilitates reasoning and understanding about the CPS design. Adapted from (COMPASS D11.3).

Architecture

The CyPhERS project considers architecture to be the “most abused term in engineering” (CyPhERS D4.1). They state that there is not “a precise definition but there is a generic consensus that an architecture is a “structural” concept and that it refers to a set of interconnected components. In the electrical world, the interconnections can be busses, wires, wireless communication channels. In the mechanical world, the interconnections are the gears, the joints, the articulation points. An architecture is most often related to physical structures, but it can also be intended in an abstract sense, where the components can be functions and the interconnections the relations between variables of the functions” (CyPhERS D4.1). The authors then provide a definition of architecture as

“a netlist of possibly abstract components, where the netlist describes how the variables of the components are related to each other” (CyPhERS D4.1).

DoDAF provide a more comprehensive definition of the term that also encompasses principles and guidelines: “the structure of components, their relationships, and the principles and guidelines governing their design and evolution over time” (DoDAF, 2007).

Greefhorst and Proper have a more property and requirement oriented definition: “those properties of an artifact that are necessary and sufficient to meet its essential requirements” (Greefhorst and Proper, 2011).

Architecture Description Language (ADL)

“conventions, principles and practices for the description of architectures established within a specific domain of application and/or community of stakeholders” (ISO/IEC 42010). There are many ADLs, with different features, created specifically for different domains, and so descriptions of CPS architectures need to cope with heterogeneity. Adapted from (COMPASS D11.3).

Architecture principle

“A design principle included in an architecture. As such, it is a declarative statement that normatively prescribes a property of the design of an artifact, which is necessary to ensure that the artifact meets its essential requirements” (Greefhorst and Proper, 2011).

Assurance

The process of providing evidence that a design is valid. Evidence can include formal proofs or exhaustive tests (constructed manually or by formal verification techniques), simulation traces, and tests of prototypes” (Asare et al.).

Certification

“Systems that determine, based on the principles of science, engineering and measurement theory, whether an artifact satisfies accepted, well-defined and measurable criteria” (CPS-VO).

Enterprise architecture

“The architecture of an enterprise. As such, it concerns those properties of an enterprise that are necessary and sufficient to meet its essential requirements” (Greefhorst and Proper, 2011).

See also enterprise systems architecting.

Enterprise systems architecting

“Enterprise Systems Architecting is a strategic approach which takes a systems perspective, viewing the entire enterprise as a holistic system encompassing multiple views such as organization view, process view, knowledge view, and enabling information technology view in an integrated framework” (Nightingale & Rhodes 2004).

See also enterprise architecture.

Ergonomics

Ergonomics (or human factors) is the scientific discipline concerned with the understanding of the interactions among humans and other elements of a system, and the profession that applies theoretical principles, data and methods to design in order to optimize human well being and overall system performance” (IEA 2010).

Ergonomics (or human factors) “discovers and applies information about human behaviour, abilities, limitations, and other characteristics to the design of tools, machines, systems, tasks, jobs, and environments for productive, safe, comfortable, and effective human use” (Sanders and McCormick 1993).

Human factors

See ergonomics.

Incremental certification

“the ability to integrate or replace new subsystems and technologies without having to re-certify the entire system to avoid repeating high costs” (CyPhERS D4.2).

See also certification.

Ontology

In the context of architecting, ontology is defined as “an element of an architectural framework that defines all the concepts and terms (ontology elements) that relate to any architecture structured according to the architectural framework” (COMPASS D11.3).

Ontology Element

In the context of architecting, ontology elements are defined as “the concepts that make up an ontology. Ontology elements can be related to each other and are used in the definition of each viewpoint (through the viewpoint elements that make up a viewpoint). The provenance for ontology elements is provided by one or more standards” (COMPASS D11.3).

Organization

Defined in (Holt & Perry, 2008) as being made up of people and facilities, which are the systems or services available. Taken from (COMPASS D11.3).

Perspective

In the context of architecting, a perspective is defined as “a collection of views (and hence also their defining viewpoints) that are related by their purpose. That is, views which address the same architectural needs, rather than being related in some other way, such as by mode of visualisation, for example” (COMPASS D11.3).

Architectural frameworks are inconsistent with each other in their terminology for perspectives, some use the term “viewpoint” or “view” in its place. We advocate the use of this terminology to avoid confusion.

See also view, viewpoint.

Process

In the context of architecting, “a process describes the approach that will be adopted to achieve some end point. A process is made up of one or more activity, one or more artefact, and one or more stakeholder” (COMPASS D11.3).

Reference Architecture

“A generalized architecture, based on best-practices” (Greefhorst and Proper, 2011).

Refinement

In the context of architecting, refinement is “the process of transforming one model element (such as views or view elements) into one or more other model elements that are closer to the target solution model, often but not exclusively at a lower level of abstraction. Refinement will typically take place in two ways:

  • Between different levels of abstraction, for example between the requirements-level views and architectural-level views, where transformation occurs.
  • In a single level of abstraction where more detailed aspects of the model are explored, for example, from use cases to scenarios in the requirement-level views, or between scenarios where transformation occurs” (COMPASS D22.5)

Adapted from (COMPASS D11.3).

Refinable Element

A Refinable Element represents a model element that may be refined (transformed) into one or more other model elements. A Refinable Element may be an element of an Architecture (such as a View or View Element) or of an Architectural Framework (such as a Viewpoint or Viewpoint Element).

Rule

In the context of architecting, “rules can be applied to need descriptions. This helps to minimise ambiguity in natural language descriptions of needs. Rules may apply to the need itself or, more usually, to the properties of a need. For example, rules may specify how a need description must be applied or the complexity of the text description of a need. Rules can also be used to constrain architectural frameworks and refinement points” (COMPASS D11.3).

Scenario

In the context of architecting, “a scenario is defined as an exploration of a use case. Each use case will give rise to a number of different situations that may arise. […] A scenario may be realised through, for example, sequence diagrams that show interactions between elements in the system, through using text as a set of scenario steps, or a combination of the two. The formality of the scenario can be increased by using parametric constraints and their usages to permit a mathematically-based approach to understanding the use cases. Scenarios may be created for specific contexts.” (COMPASS D11.3)

Service oriented architecture (SOA)

A service-oriented architecture is based on the notion of a client and server, in which “a server represents a process that provides services to other processes (the clients). Usually the server does not know in advance the identities of number of clients that will access it at run time. On the other hand, clients know the identity of the server (or can find it through some other server) and access it by remote procedure call” (Garlan & Shaw 1994). Taken from (COMPASS D11.3).

View

“A view is the visualisation of part of the architecture of a system, that conforms to the structure and content defined in a viewpoint” (COMPASS D11.3).

A view is the instantiation of a viewpoint for a particular system (or CPS).

Some architectural frameworks do not distinguish between views and viewpoints, we advocate the use of this terminology to avoid confusion.

See also viewpoint, perspective.

View consistency

“presentation of reliable and uniform views on concurrency effects, system composition as well as fulfilment of assumption and constraints within in the SoS architecture.” (COMPASS D22.1).

Viewpoint

“A viewpoint defines the structure and content of a view. […] It uses the concepts and terms from an ontology […]. Each viewpoint is defined so that it meets the needs defined by its viewpoint concerns” (COMPASS D11.3).

Architectural frameworks are inconsistent with each other in their terminology for viewpoints, some use the term “view” or “subview” in its place. We advocate the use of this terminology to avoid confusion.

See also view, perspective.

  • Systems design, modelling and virtual engineering for cyber-physical systems

This theme is especially concerned with increasingly complex modelling of increasingly complex system. Autonomous systems interacting with humans will require new developments in M&S that should be extended to reliable verification and validation; this also links to Theme 1. A feature of this aspect of modelling will be dynamic models that capture accurately self-organising systems containing embedded software. Virtual engineering as a means to explore more extensive solution spaces will also be a feature of this theme.

Design space

“The set of possible solutions for a given design problem” (Fitzgerald et al., 2014).

Virtual engineering

In virtual engineering “geometric modeling systems, computer graphics, CAE and CAM systems are all applied during the product development process” (Lee, 1999).

“Virtual engineering requires behaviour models (for example, how to define the emergency procedures and flows for a ship or a stadium evacuation), physical models (geometrical, kinematic, dynamic, finite element models), anthropometric representations (for ergonomic evaluation), visual and textured models (rapid three-dimensional visualization for games, clothes representation, etc.), and many others for a realistic representation of human mannequin for in situ use” (Bernard, 2005).

  • Real time modelling for autonomous adaptive and cooperative cyber-physical systems

This theme is concerned with models that can be used to control dynamic systems, such that they are more efficient in the use of resources and adapt appropriately over the life-cycle to ensure sustainability. This theme will also include aspects of machine learning and distributed decision making by CPS. Human machine interfaces will also be a significant consideration in this theme.

Adaptive

Adaptive (Cyber-Physical) Systems “adapt to their users and to new situations. In other words, they learn what the user is trying to achieve in a given situation and how they wish to operate the system and they adapt to the user’s language” (CyPhERS D5.1)

Data mining

Relates to machine learning, which “involves the use of information technology and mathematical theory to enable computers to extract knowledge from the available data” (CyPhERS D5.1). For data mining “this may be done […] to generate completely new knowledge” (CyPhERS D5.1).

See also machine learning.

Ergonomics

Ergonomics (or human factors) is the scientific discipline concerned with the understanding of the interactions among humans and other elements of a system, and the profession that applies theoretical principles, data and methods to design in order to optimize human well being and overall system performance” (IEA 2010).

Ergonomics (or human factors) “discovers and applies information about human behaviour, abilities, limitations, and other characteristics to the design of tools, machines, systems, tasks, jobs, and environments for productive, safe, comfortable, and effective human use” (Sanders and McCormick 1993).

Human factors

See ergonomics.

Machine learning

“involves the use of information technology and mathematical theory to enable computers to extract knowledge from the available data. This may be done in order to find the answer to a specific question (“what does a typical traffic jam look like?”)” (CyPhERS D5.1).

See also data mining.

Real-time coordination

“Coordinating individual systems to function dynamically and simultaneously in all situations” (CPS-VO).

Real-time system

A system that is “able to process data as it comes in, typically without buffering delays” (CPS-VO).

Time synchronization

“Coordinating clocks in multiple devices to function simultaneously” (CPS-VO).

  • MBSE applied to computing platforms and energy management

This theme is concerned with energy efficient computing and includes the better management of large distributed networks of devices. The emphasis of this theme will be on the use of MBSE to describe, and hence manage large networks that dynamically reconfigure. Environmental modelling will also be important in this theme.

Cyber-physical cloud computing

Similar in spirit to virtual machines, virtual vehicles provide a robust, mobile, secure, and safe execution and information acquisition platform enabling what we call cyber-physical cloud computing (CPCC). Here, cloud computing becomes a metaphor for information acquisition as a service of mobile sensor networks, rather than the traditional notion of platform- or software-as-a service” (Craciunas et al., 2010).

  • Integration of socio/legal/governance models within modelling frameworks

Models of technical systems must necessarily make assumptions about the operational environment and the rules of operation. However, to better understand the complexities of massive CPS in the everyday world, models must be developed that include social, legal, and governance aspects of the overall system. This is an area of growing importance both for safe operation and for understanding better how the full power of CPS can be exploited. This theme will focus on the integration of heterogeneous models that afford an integration of embedded software with models of the real work in which they operate.

Acceptance

Acceptance is related to “the willingness of users to adopt or refuse a new technology” (CyPhERS D2.1).

Competence (person)

“An ability to achieve a given task or job.

[Relating to human activities] We define competence as the ability exhibited by a person that is made up of a set of one or individual competencies.” (COMPASS D11.3).

Competency

“The representation of a single skill that contributes towards making up a competence.” (COMPASS D11.3).

Competency area

“Competency area is a grouping of related competency, such as those related to requirements engineering or to architectures.” (COMPASS D11.3).

Competency profile

“Shows the actual abilities that are possessed by a specific person. The competency profile may be generated at the output of a competency assessment exercise that uses a competency scope as its input.” (COMPASS D11.3).

Competency Scope

“Defines the abilities that are required for a specific stakeholder role.” (COMPASS D11.3).

Critical mass

Critical mass “­can be interpreted as the number of people adopting a certain technology” (CyPhERS D2.1).

Digital divide

The digital divide “consists of ‘differences due to geography, race, economic status, gender and physical ability in access to information through the Internet, and other information technologies and services, as well as in the skills, knowledge and abilities to use information, the Internet and other technologies’.” Taken from (Kanwar, 2008)[4].

Dropout

“A “dropout” is an individual who, for various reasons, decides or is forced to avoid adoption or use of a device” (CyPhERS D2.1).

Enterprise architecture

“The architecture of an enterprise. As such, it concerns those properties of an enterprise that are necessary and sufficient to meet its essential requirements” (Greefhorst and Proper, 2011).

See also enterprise systems architecting.

Enterprise engineering

“The creative application of scientific principles to develop (which includes design and implementation) enterprises, or parts/aspects thereof; or to operate the same with full cognizance of their design; or to forecast their behavior under specific operating conditions; all as respects an intended function, economics of operation and safety to life and property” (Greefhorst and Proper, 2011).

See also enterprise systems architecting.

Enterprise systems architecting

“Enterprise Systems Architecting is a strategic approach which takes a systems perspective, viewing the entire enterprise as a holistic system encompassing multiple views such as organization view, process view, knowledge view, and enabling information technology view in an integrated framework” (Nightingale & Rhodes 2004).

See also enterprise architecture.

Ergonomics

Ergonomics (or human factors) is the scientific discipline concerned with the understanding of the interactions among humans and other elements of a system, and the profession that applies theoretical principles, data and methods to design in order to optimize human well being and overall system performance” (IEA 2010).

Ergonomics (or human factors) “discovers and applies information about human behaviour, abilities, limitations, and other characteristics to the design of tools, machines, systems, tasks, jobs, and environments for productive, safe, comfortable, and effective human use” (Sanders and McCormick 1993).

Gate

“A gate is a special type of review that must be executed before any one stage may be exited. A gate assesses the execution of a stage” (COMPASS D11.3).

Governance

Many definitions of governance in computing are concerned with many aspects of the business, including items such as delivery of business value (Webb et al, 2006). With an emphasis on modelling and simulation across organisational boundaries, we have an interest in control and accountability as well as business value. Therefore we adapt a definition from Kingsford et al (2003):

The governance of a CPS comprises the rules or guidelines that determine the division of roles, responsibilities and accountabilities and how decisions are made. Adapted from (COMPASS D11.3).

Human factors

See ergonomics.

Indicator

“A feature of a competency that describes knowledge, skill or attitude required to meet the competency. It is the indicator that is assessed as part of competency assessment” (COMPASS D11.3).

Organization

Defined in (Holt & Perry, 2008) as being made up of people and facilities, which are the systems or services available. Taken from (COMPASS D11.3).

Perspective

In the context of architecting, a perspective is defined as “a collection of views (and hence also their defining viewpoints) that are related by their purpose. That is, views which address the same architectural needs, rather than being related in some other way, such as by mode of visualisation, for example” (COMPASS D11.3).

Stage

“A stage represents a discrete time period that describes a specific phase of a life cycle. Stages are typically defined by the context in which the life cycle is being used. Before a stage can be exited for any reason, it must pass through a gate.” (COMPASS D11.3)

T-shaped person

“A metaphor referring to a combination of skills, where the vertical bar of the T represents depth of knowledge and skills in a particular area, and where the horizontal bar refers to cross- disciplinary collaboration skills, implying communication and collaboration skills as well as perspective beyond the depth of the vertical specialization” (CyPhERS D5.2).

 

 

[1] http://www.compass-research.eu/

[2] http://www.cyphers.eu/

[3] Adapted from D. Shetty and R.A. Kolk, Mechatronics System Design. PWS Publishing Company, 1997.

 

[4] The paper attributes the quote to an online source (), which is no longer available.